MODELS OF OLDER ADULT GROUP ENGAGEMENT TO IMPROVE HEALTH MANAGEMENT
Bibliographic record
Abstract
Abstract We are studying the use of peer-to-peer group intervention as a means of promoting older adult health self-efficacy and self-management. To explore how older adults have worked together to improve health behaviors, a scoping review was conducted of older adult peer coaching in health maintenance or health improvement groups. Seventeen studies met all search criteria, including interventions examining the value of peer support in self-management of diabetes, a peer led program for fear of falling, and the effect of self-help groups on quality of life. Two models of peer engagement were identified: peer support and mutually supportive environments. Ten studies trained older adults to be peer mentors or leaders with training periods varying from two days to 30 weeks, although many did not include details of the training. The other seven studies examined mutually supportive environments for peer engagement such as a clinician-led with peer-support model, an app-based program with a social support component, and a prevention focused mutual support group. These studies included research comparing self-care and quality of life results after self-help group therapy and a study that analyzed the impact and role of volunteering at a seniors’ centre on maximizing member self-efficacy. While all studies reported on peer self-health engagement, there were many different goals ranging from evaluating health improvement programs to comparing peer and professional health group leadership. One consistent theme was improved perceived self-efficacy though peer group engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".